The invention relates to estimating the attitude of a spacecraft such as a satellite by means of a star sensor mounted on such a craft, and the invention relates more precisely to reducing errors that appear in such attitude estimation.
More precisely, the present invention relates to reducing so-called “field of view” (FOV) errors.
Nowadays, the performance expected of an observation satellite is very ambitious in terms of absolute localization: for example, accuracy of 10 meters (m) is required in 90% of cases and of 20 m in 99.7% of cases. The main factors contributing to such performance are error in determining attitude, and also thermoelastic deformation between the instrument line of sight and the attitude sensors.
For example, it is possible to define and propose a “gyrostellar” attitude measuring system based on three very high performance fiber optic gyros (FOGs) and three wide-angle (≈25°) star sensors.
Star sensors present various sources of error in measuring attitude, which can be classified depending on their time nature. Generally, distinctions are made between bias, harmonics, FOV error, and white noise. The impacts of these various time categories on the performance of the attitude-determining subassembly are different. In particular, gyrostellar filtering serves to reduce the impact of the “noise” category. The harmonic category is essentially of thermoelastic origin, and its impact can be limited by controlling the temperature environment of the sensor. The bias category has small impact insofar as calibration means can be implemented.
The FOV error of a star sensor has greatest impact on performance in determining the attitude of such a star sensor or of the spacecraft on which it is mounted. This leads to specifications being issued that are quite severe concerning star sensors and for which there has been no response until now. Indeed specifications have been relaxed in order to comply with that which is available.
In this context, analysis of results shows that FOV error can be assumed to be 10 microradians (μrad) in a configuration comprising 3 FOGs+3 standard star trackers (SSTs), which error can be reduced to 6 μrad by the gyrostellar estimator filter. This error, combined quadratically with other factors, gives rise to an overall attitude and orbit control system (AOCS) end result of 11 μrad, for an allocation of 13 μrad. Configurations with 2 or 1 SSTs lead to degraded performance. These observations have been confirmed by statistical results obtained from simulations, and have led to a present tightening of FOV error specifications as issued to the suppliers of star sensors.
FOV errors lead to phenomena such as incomplete knowledge of and fluctuation in focal length, distortion calibration residues, chromatic aberrations, and errors in knowledge about the absolute positions of stars (catalog errors, relativistic correction residues). They lead to errors in measuring the position of each star in the focal plane, said errors being of amplitudes and directions that vary as a function of the position of the star in the field of view. Wide-angle star sensors track a large number of stars simultaneously (typically in the range about ten to a few tens). The resulting attitude measurement error depends on the distribution of stars in the field of view and on the way it varies over time under the effect of the angular speed of the carrying satellite.
In the context of geocentric pointing, and particularly for a satellite that is maneuvering, induced determination error can be modeled as noise filtered by a lowpass filter-having a time constant associated with the mean time taken by a star to pass through the field of view. Because of the resulting relatively large time constants that stem therefrom (about 100 seconds), such noise is difficult to reduce by the attitude estimator filter which combines star measurements with gyro measurements.
With inertial pointing (e.g. solar pointing), this substantially constant measurement error is no longer filtered at all by the attitude estimator algorithm.
Furthermore, such a model of field of view errors is somewhat theoretical, and complete simulations associating fine modeling of the sensor with dynamic modeling would be needed to quantify such errors more precisely.
In parallel, it therefore appears appropriate to pursue lines of investigation that enable this source of error to be reduced without impact on the hardware architecture of existing sensors, for example by means of an inline calibration algorithm.